Questions buyers ask. Before an AI project.
Straight answers to the questions we hear before a project starts: what we build, how we scope it, what it costs to run, who owns the result, and where people stay in control. Each answer links to the page with the details.
About Agents Autonomous
What does Agents Autonomous do?
We work across AI services: getting more out of the tools you already license, connecting workflows across the systems you keep, and building AI agents or custom software when nothing off the shelf fits. Focused services cover reporting, document processing, customer support, and sales workflows, with evaluation and training as separate scopes. Every engagement starts with one task and its owner.
More on thisWe already pay for AI tools. What would you actually do?
Look at what those licenses do today: who uses them, for which tasks, and where the answers fall short. The work is usually some combination of connecting approved sources, training specific roles on their own tasks, and wiring the assistant into the system where the work is recorded. We start with what you have; if a configuration change solves the problem, we will not propose a custom build.
More on thisWho will we work with?
You’ll work with our team, with co-founders Andrew Matusevich and Seva Rybakov leading project discussions and staying involved throughout delivery. Andrew leads the business case, architecture, and operation; Seva leads agent engineering and integrations.
More on thisWhere do you work?
We build for companies in the United States. Project conversations, reviews, and handoffs run online with your workflow owner involved from the first scope.
Book a call ↗Do you publish client case studies?
Project profiles and case studies are listed in Use cases. Ask us about available client references; we share them only with permission.
More on thisScoping a project
What makes a good first AI or automation project?
A repeated task with accessible examples, a clear output, and someone who can judge the result. Define its beginning and end, the tools involved, and the exceptions that need a person. That makes it possible to agree acceptance criteria before the build.
More on thisDo we need to choose between an agent, automation, or custom software first?
No. Describe the task and the tools it touches. Rules-based automation, a knowledge assistant, a tool-using agent, and a custom application solve different problems; the choice follows from how much judgment the task needs and how reversible its actions are.
More on thisIs this just ChatGPT for the team?
It can start with helping people use ChatGPT or another assistant well. For work shared across a team, we also check sources, handoffs, and approvals. We agree whether setup and training are enough or a connected workflow is needed.
More on thisWhat do we have to write down?
Enough that the next run knows the goal, the sources, the checks, and when to stop. That operating record is part of the handoff, not an extra slide deck.
More on thisHow long will the project take?
The timeline is agreed for the specific scope. Access to systems, ready source material, unresolved business rules, and the availability of reviewers all affect delivery. The plan separates a feasibility prototype from a workflow ready for everyday operation.
More on thisCost and ownership
How much does an AI or automation project cost?
Price depends on the workflow, source quality, integrations, permissions, and review requirements. A scoped proposal separates implementation from recurring model, hosting, platform, and support costs. Describe one task first so the estimate is based on the work and its dependencies.
More on thisWhat does an AI workflow cost to run?
Measure cost per accepted task: model calls, tools, infrastructure, retries, and review labor divided by the results a reviewer accepted. Token prices alone hide retries and review time. The guide and the calculator show the method.
More on thisWho owns and maintains the result?
Code ownership, licensing, hosting, access, and continuing support are terms to agree before work starts. The handoff names the owner for source updates, failures, credentials, and changes. Maintenance is part of the engagement only when it is explicitly included.
More on thisWhich AI models and providers do you use?
We choose the model and provider for each project based on the task, the data-handling terms, and cost, and we name them in the scope. Different steps of one workflow may use different models.
More on thisData, security, and control
Can we use the software and data we already have?
Assess the existing tools and sources first. APIs, exports, account permissions, data quality, and provider terms determine what can be connected. Begin with an outline or redacted examples; agree a sharing method before providing credentials or sensitive records.
More on thisIs our data used to train AI models?
That depends on the provider, the plan, and the configuration. Business and API terms from major providers generally exclude training on customer data by default, but the actual setting is checked and recorded for each service in the data path.
More on thisDo you replace the people who know the work?
No. They choose the job and they review what matters. AI prepares the middle. If a task should not be automated, we say so.
More on thisHow do you work with our internal IT team?
IT stays in charge of your systems. We ask which accounts, licenses, and integrations already exist, agree the least privilege a workflow needs, and route access requests through their normal process rather than around it. Where your team prefers to own the build, we can scope the design and the acceptance tests instead. Whatever we hand over comes with the operating record IT needs to run it.
More on thisWhat can an agent do without asking?
Only what the permission matrix allows: typically reading approved sources and preparing drafts or records. Sending, filing, paying, deleting, and changing customer data wait for an approval gate unless the action is reversible and the owner has agreed otherwise.
More on thisTesting and results
How will we know whether the result is good enough?
Agree test cases and acceptance criteria with the person who owns the workflow. Check expected outputs, evidence, incomplete inputs, and permission boundaries. Review recorded failures as well as successful runs, then define the monitoring for the first rollout.
More on thisWhich metrics do you use?
The ones that fit the task: correctness, completeness, harmful actions, review burden, latency, or cost per completed task. Thresholds are agreed for the project, not invented in advance.
More on thisDoes passing tests mean the system cannot fail?
No. Tests cover the cases examined. Monitoring, human review, and a recovery plan are still needed for real operation.
More on thisHow do we start?
Book a thirty-minute conversation and bring one workflow. If you want a head start, download the one-page project brief and answer its questions first.
Book a call ↗